Key result
Modeled 7-center PCI network matches STEMI access of 11 existing centers.
Why the study?
Access to PCI centres for STEMI patients has traditionally been determined by historical referral patterns and arbitrary geographic boundaries, lacking predictive modeling of demand and access.
p-value: p=0.18
7-center model may match 11-center STEMI access; leaves open reconfiguration feasibility pending prospective validation.
BACKGROUND: Access to individual percutaneous coronary intervention (PCI) centres has traditionally been determined by historical referral patterns along arbitrarily defined geographic boundaries. We set out to produce predictive models of ST-elevation myocardial infarction (STEMI) demand and time-efficient access to PCI centres. METHODS: Travel times from random addresses to PCI centres in Melbourne, Australia, were estimated using Google map application programming interface (API). Departures at 08:15 and 17:15 were compared with 23:00 to determine the effect of peak hour traffic congestion. Real-world ambulance travel times were compared with estimated travel times using Google map developer software. STEMI incidence per postcode was estimated by merging STEMI incidence per age group data with age group per postcode census data. PCI centre network configuration changes were assessed for their effect on hospital STEMI loading, catchment size, travel times and the number of STEMI cases within 30 min of a PCI centre. RESULTS: Nearly 10% of STEMI cases travelled more than 30 min to a PCI centre, increasing to 20% by modelling the removal of large outer metropolitan PCI centres (p<0.05). A model of 7 PCI centres compared favourably to the current existing network of 11 PCI centres (p=0.18 (afternoon), p=0.5 (morning and night)). The intraclass correlation between estimated travel times and ambulance travel times was 0.82, p<0.001. CONCLUSION: This paper provides a framework to integrate prehospital environmental variables, existing or altered healthcare resources and health statistics to objectively model STEMI demand and consequent access to PCI. Our methodology can be modified to incorporate other inputs to compute optimum healthcare efficiencies.
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Cole et al. (2021) studied ST-elevation myocardial infarction (STEMI). Model of 7 PCI centres vs. Current existing network of 11 PCI centres was evaluated on Travel times and STEMI demand (p=0.18). A predictive model of 7 PCI centres compared favorably to the existing network of 11 centres for STEMI access (p=0.18 in the afternoon, p=0.5 in the morning and night).
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